A Decentralized Framework for Resource-Constrained Task Redistribution

Abstract

Earth-observing satellite constellations are increasingly expected to operate autonomously in dynamic, resource-constrained, and failure-prone environments. As constellation size and heterogeneity grow, centralized and static task scheduling paradigms struggle to provide the adaptability required to maintain mission continuity under disruptions, intermittent connectivity, and limited onboard storage. This paper presents a decentralized task rescheduling framework inspired by a service-industry scheduling analogy that models satellites as resource constrained agents operating under memory restrictions, spatial access limitations, and intermittent offloading opportunities. A multi-criteria redistribution algorithm prioritizes memory availability, proximity, capability, and workload to reassign tasks following agent loss. Through large-scale simulation, we demonstrate that the proposed framework preserves high-priority task throughput and fair workload distribution despite irreversible capacity loss. The resulting system provides a physically motivated abstraction for studying resilient, decentralized scheduling and establishes a bridge between intuitive heuristic policies and future learning-based autonomy for heterogeneous low Earth orbit satellite constellations.